



"""simple docstring"""





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np
def 						__lowerCAmelCase    (		__UpperCamelCase   :      bytes       ,			__UpperCamelCase   :      int     ):


						'''simple docstring'''






						snake_case_ :			Optional[Any]            =      F'{sampling_rate}'
						snake_case_ :			Dict            =      """1"""
						snake_case_ :			int            =      """f32le"""
						snake_case_ :			Dict            =      [
						    """ffmpeg""",
						    """-i""",
						    """pipe:0""",
						    """-ac""",
						    ac,
						    """-ar""",
						    ar,
						    """-f""",
						    format_for_conversion,
						    """-hide_banner""",
						    """-loglevel""",
						    """quiet""",
						    """pipe:1""",
						]

						try:
												with subprocess.Popen(__UpperCamelCase       ,			stdin=subprocess.PIPE       ,			stdout=subprocess.PIPE     ) as ffmpeg_process:
																		snake_case_ :			Tuple            =      ffmpeg_process.communicate(__UpperCamelCase     )
						except FileNotFoundError as error:
												raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""     ) from error
						snake_case_ :			Optional[int]            =      output_stream[0]
						snake_case_ :			Optional[Any]            =      np.frombuffer(__UpperCamelCase       ,			np.floataa     )
						if audio.shape[0] == 0:
												raise ValueError("""Malformed soundfile"""     )
						return audio
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int       ,			__UpperCamelCase   :      float       ,			__UpperCamelCase   :      str = "f32le"       ,			):


						'''simple docstring'''






						snake_case_ :			Any            =      F'{sampling_rate}'
						snake_case_ :			Tuple            =      """1"""
						if format_for_conversion == "s16le":
												snake_case_ :			List[Any]            =      2
						elif format_for_conversion == "f32le":
												snake_case_ :			List[Any]            =      4
						else:
												raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'     )

						snake_case_ :			str            =      platform.system()
						if system == "Linux":
												snake_case_ :			Any            =      """alsa"""
												snake_case_ :			Dict            =      """default"""
						elif system == "Darwin":
												snake_case_ :			List[Any]            =      """avfoundation"""
												snake_case_ :			Optional[int]            =      """:0"""
						elif system == "Windows":
												snake_case_ :			int            =      """dshow"""
												snake_case_ :			int            =      """default"""

						snake_case_ :			List[Any]            =      [
						    """ffmpeg""",
						    """-f""",
						    format_,
						    """-i""",
						    input_,
						    """-ac""",
						    ac,
						    """-ar""",
						    ar,
						    """-f""",
						    format_for_conversion,
						    """-fflags""",
						    """nobuffer""",
						    """-hide_banner""",
						    """-loglevel""",
						    """quiet""",
						    """pipe:1""",
						]
						snake_case_ :			str            =      int(round(sampling_rate * chunk_length_s     )     ) * size_of_sample
						snake_case_ :			str            =      _ffmpeg_stream(__UpperCamelCase       ,			__UpperCamelCase     )
						for item in iterator:
												yield item
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int       ,			__UpperCamelCase   :      float       ,			__UpperCamelCase   :      Optional[int] = None       ,			__UpperCamelCase   :      Optional[Union[Tuple[float, float], float]] = None       ,			__UpperCamelCase   :      str = "f32le"       ,			):


						'''simple docstring'''






						if stream_chunk_s is not None:
												snake_case_ :			Tuple            =      stream_chunk_s
						else:
												snake_case_ :			Dict            =      chunk_length_s

						snake_case_ :			Union[str, Any]            =      ffmpeg_microphone(__UpperCamelCase       ,			__UpperCamelCase       ,			format_for_conversion=__UpperCamelCase     )
						if format_for_conversion == "s16le":
												snake_case_ :			Tuple            =      np.intaa
												snake_case_ :			int            =      2
						elif format_for_conversion == "f32le":
												snake_case_ :			Tuple            =      np.floataa
												snake_case_ :			Optional[Any]            =      4
						else:
												raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'     )

						if stride_length_s is None:
												snake_case_ :			List[Any]            =      chunk_length_s / 6
						snake_case_ :			Optional[int]            =      int(round(sampling_rate * chunk_length_s     )     ) * size_of_sample
						if isinstance(__UpperCamelCase       ,			(int, float)     ):
												snake_case_ :			Union[str, Any]            =      [stride_length_s, stride_length_s]

						snake_case_ :			Dict            =      int(round(sampling_rate * stride_length_s[0]     )     ) * size_of_sample
						snake_case_ :			Union[str, Any]            =      int(round(sampling_rate * stride_length_s[1]     )     ) * size_of_sample
						snake_case_ :			str            =      datetime.datetime.now()
						snake_case_ :			int            =      datetime.timedelta(seconds=__UpperCamelCase     )
						for item in chunk_bytes_iter(__UpperCamelCase       ,			__UpperCamelCase       ,			stride=(stride_left, stride_right)       ,			stream=__UpperCamelCase     ):
												# Put everything back in numpy scale
												snake_case_ :			Any            =      np.frombuffer(item["""raw"""]       ,			dtype=__UpperCamelCase     )
												snake_case_ :			Dict            =      (
												    item["""stride"""][0] // size_of_sample,
												    item["""stride"""][1] // size_of_sample,
												)
												snake_case_ :			Optional[int]            =      sampling_rate
												audio_time += delta
												if datetime.datetime.now() > audio_time + 1_0 * delta:
																		# We're late !! SKIP
																		continue
												yield item
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Optional[int]       ,			__UpperCamelCase   :      int       ,			__UpperCamelCase   :      Tuple[int, int]       ,			__UpperCamelCase   :      bool = False     ):


						'''simple docstring'''






						snake_case_ :			Dict            =      B""""""
						snake_case_      ,		snake_case_ :			List[Any]            =      stride
						if stride_left + stride_right >= chunk_len:
												raise ValueError(
												    F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'     )
						snake_case_ :			List[str]            =      0
						for raw in iterator:
												acc += raw
												if stream and len(__UpperCamelCase     ) < chunk_len:
																		snake_case_ :			Any            =      (_stride_left, 0)
																		yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
												else:
																		while len(__UpperCamelCase     ) >= chunk_len:
																								# We are flushing the accumulator
																								snake_case_ :			Any            =      (_stride_left, stride_right)
																								snake_case_ :			Optional[Any]            =      {"""raw""": acc[:chunk_len], """stride""": stride}
																								if stream:
																														snake_case_ :			List[Any]            =      False
																								yield item
																								snake_case_ :			Optional[int]            =      stride_left
																								snake_case_ :			Dict            =      acc[chunk_len - stride_left - stride_right :]
    # Last chunk
						if len(__UpperCamelCase     ) > stride_left:
												snake_case_ :			str            =      {"""raw""": acc, """stride""": (_stride_left, 0)}
												if stream:
																		snake_case_ :			Dict            =      False
												yield item






def 						__lowerCAmelCase    (		__UpperCamelCase   :      Any       ,			__UpperCamelCase   :      int     ):


						'''simple docstring'''






						snake_case_ :			List[str]            =      2**2_4  # 16Mo
						try:
												with subprocess.Popen(__UpperCamelCase       ,			stdout=subprocess.PIPE       ,			bufsize=__UpperCamelCase     ) as ffmpeg_process:
																		while True:
																								snake_case_ :			List[str]            =      ffmpeg_process.stdout.read(__UpperCamelCase     )
																								if raw == b"":
																														break
																								yield raw
						except FileNotFoundError as error:
												raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""     ) from error


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Optional[Any]         =    logging.get_logger(__name__)

__a    :	Optional[Any]         =    torch.device("""cpu""")





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase					     =				Image.open(requests.get(lowercase				,     stream=lowercase				).raw				)
				return im





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				if swiftformer_name == "swiftformer_xs":
								return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]				)

				elif swiftformer_name == "swiftformer_s":
								return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]				)

				elif swiftformer_name == "swiftformer_l1":
								return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]				)

				elif swiftformer_name == "swiftformer_l3":
								return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]				)





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				dct.pop(lowercase				)
				__lowercase					     =				val





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				[]
				for k in state_dict.keys():
								__lowercase					     =				k
								if ".pwconv" in k:
												__lowercase					     =				k_new.replace('''.pwconv'''				,     '''.point_wise_conv'''				)
								if ".dwconv" in k:
												__lowercase					     =				k_new.replace('''.dwconv'''				,     '''.depth_wise_conv'''				)
								if ".Proj." in k:
												__lowercase					     =				k_new.replace('''.Proj.'''				,     '''.proj.'''				)
								if "patch_embed" in k_new:
												__lowercase					     =				k_new.replace('''patch_embed'''				,     '''swiftformer.patch_embed.patch_embedding'''				)
								if "network" in k_new:
												__lowercase					     =				k_new.split('''.'''				)
												if ls[2].isdigit():
																__lowercase					     =				'''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]				)
												else:
																__lowercase					     =				k_new.replace('''network'''				,     '''swiftformer.encoder.network'''				)
								rename_keys.append((k, k_new)				)
				return rename_keys





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				SwiftFormerConfig()

				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				__lowercase					     =				1000
				__lowercase					     =				'''huggingface/label-files'''
				__lowercase					     =				'''imagenet-1k-id2label.json'''
				__lowercase					     =				json.load(open(hf_hub_download(lowercase				,     lowercase				,     repo_type='''dataset'''				)				,     '''r'''				)				)
				__lowercase					     =				{int(lowercase				): v for k, v in idalabel.items()}
				__lowercase					     =				idalabel
				__lowercase					     =				{v: k for k, v in idalabel.items()}

				# size of the architecture
				if swiftformer_name == "swiftformer_xs":
								__lowercase					     =				[3, 3, 6, 4]
								__lowercase					     =				[48, 56, 112, 220]

				elif swiftformer_name == "swiftformer_s":
								__lowercase					     =				[3, 3, 9, 6]
								__lowercase					     =				[48, 64, 168, 224]

				elif swiftformer_name == "swiftformer_l1":
								__lowercase					     =				[4, 3, 10, 5]
								__lowercase					     =				[48, 96, 192, 384]

				elif swiftformer_name == "swiftformer_l3":
								__lowercase					     =				[4, 4, 12, 6]
								__lowercase					     =				[64, 128, 320, 512]

				# load state_dict of original model, remove and rename some keys
				if original_ckpt:
								if original_ckpt.startswith('''https'''				):
												__lowercase					     =				torch.hub.load_state_dict_from_url(lowercase				,     map_location='''cpu'''				,     check_hash=lowercase				)
								else:
												__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				__lowercase					     =				checkpoint

				__lowercase					     =				create_rename_keys(lowercase				)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(lowercase				,     lowercase				,     lowercase				)

				# load HuggingFace model
				__lowercase					     =				SwiftFormerForImageClassification(lowercase				).eval()
				hf_model.load_state_dict(lowercase				)

				# prepare test inputs
				__lowercase					     =				prepare_img()
				__lowercase					     =				ViTImageProcessor.from_pretrained('''preprocessor_config'''				)
				__lowercase					     =				processor(images=lowercase				,     return_tensors='''pt'''				)

				# compare outputs from both models
				__lowercase					     =				get_expected_output(lowercase				)
				__lowercase					     =				hf_model(inputs['''pixel_values''']				).logits

				assert hf_logits.shape == torch.Size([1, 1000]				)
				assert torch.allclose(hf_logits[0, 0:5]				,     lowercase				,     atol=1E-3				)

				Path(lowercase				).mkdir(exist_ok=lowercase				)
				print(F"Saving model {swiftformer_name} to {pytorch_dump_folder_path}"				)
				hf_model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--swiftformer_name""",
		    default="""swiftformer_xs""",
		    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
		    type=str,
		    help="""Name of the SwiftFormer model you'd like to convert.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default="""./converted_outputs/""",
		    type=str,
		    help="""Path to the output PyTorch model directory.""",
		)
		parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

		__a    :	Optional[int]         =    parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)